Fuzzy Switching Median Filter Research Articles

Extraction of significant features using GLDM for Covid-19 prediction

Although Covid-19 caused by the SARS-COV-2 virus, is a deadliest disease, many people experienced mild symptoms and were recovered soon. In this paper, coronavirus can be easily detected using CT scan images of affected patients. Initially, images are pre-processed by filters like Median filter and Noise adaptive fuzzy switching median filter, and then the quality measurements like MSE, and PSNR are calculated. After preprocessing, segmentation is done by K-means and Robust self sparse fuzzy clustering algorithm, and then the parameters like LMSE and NAE are calculated. Finally, to get optimum results, feature extraction using GLDM is performed which helps in identifying whether it's a normal lung disease like pneumonia or the patient is affected by covid.

Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

DWM (Directional weighted median) filter is very popular in filtering digital image and remove mixed noise. Fuzzy logic is implemented with median filters to improve its performance. In the previous work, fuzzy logic system is implemented with switching median filter and gives better performance than directional median filter as well as switching median filter. Experimenting directional median filter with same fuzzy logic system didn’t yield to better results therefore fuzzy logic parameters has been changes as per strong points of directional weighted median filter and a constant has been included in the filtering equation to improve the results. So in this proposed work, we have successfully implemented directional weighted median filter with fuzzy logic system which is proving better results than DWM and FSMF (Fuzzy Switching Median Filter). PSNR (Peak Signal to Noise Ratio)is used for qualitative analysis of results.

Noise Reduction on Bracketed Images for High Dynamic Range Imaging

The quality of a high dynamic range (HDR) image produced from bracketed images taken at different camera exposure times can be degraded by noise contained in bracketed images. In this paper, we propose a noise reduction method on bracketed images based on exposure time ratio. First, for each pixel pair of a same scene point lying on two different images, the ratio of their intensity values is compared with the ratio of exposure times of the images on which the pixels are lying. If the compared ratios are close, these two pixels are included in noise-free pixels set. The complement of noise-free pixels set is defined as noisy pixels set. Then, the intensity value of each pixel in noisy pixels set is corrected by its expected value computed from noise-free pixel of the same scene point lying on another image. Experimental results show that all the noisy intensity values can be correctly restored from noise-free pixels except the saturated pixels. Denoising results by PSNR show that the proposed method outperforms the other recent denoising methods such as based-on pixel density filter (BPDF), noise adaptive fuzzy switching median filter (NAFSMF), and adaptive Riesz mean filter (ARmF).

Filtering impulse noise in medical images using information sets

Abstract An efficient filtering algorithm is required to remove noise and simultaneously protect fine details and important features in the medical images. In this paper, a noise adaptive information set based switching median (NAISM) filter is proposed for the removal of impulse noise. NAISM filter is inspired from fuzzy switching median filter and works on the concept of information sets. Information sets are derived from fuzzy sets to deal with the uncertainty. It works in two phases; first phase identifies noisy pixels and second applies filtering based on an adaptive switching criterion. It is by virtue of this switching criterion and the local effective information surrounding the noisy pixel, the best calculated value replaces the noisy pixel in the selected window. The proposed information set based filter is capable of removing both low and high noise densities and can preserve image details better than the fuzzy filter. The applicability of the proposed filter is demonstrated on different datasets including Berkeley Segmentation Dataset (BSD), medical and real images. The qualitative and quantitative results demonstrate the effectiveness of the proposed approach in suppressing noise over the existing approaches.

Noise Adaptive and Similarity Based Switching Median Filter for Salt & Pepper Noise

Works have been
 conducted recently to remove high intensity salt & pepper noise by virtue
 of adaptive and switching median filters. One of the cited works is the
 Noisy Adaptive Fuzzy Switching Median Filter (NAFSM) by which the noisy pixels
 are detected through utilization of image histogram. Noiseless pixels are
 left unprocessed while noisy pixels are passed through the noise adaptive
 median filter which expands for them. A filter mechanism which performs
 decision making in line with local similarity and similarity has been proposed
 for NAFSM. Local similarity information in 3x3 mask has been used for
 filtering mechanism in the study titled Noise Adaptive and Similar Based
 Switching Median Filter (NASBSM). Two thresholds with three regions were
 made by virtue of local similarity information. The logic of the approach
 was based on more intensive filtering for noisy pixels with high similarity
 value with neighboring pixels and less for those with less similarity value
 with neighboring pixels. According to the numerical and visual simulation
 results of the NASBSM mechanism, it was detected that it eliminates noises with
 high density.

A new method based on pixel density in salt and pepper noise removal

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF. The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density.

Some studies on detection and filtering algorithms for the removal of random valued impulse noise

Removing random valued impulse noise (RVIN) is a challenging task in corrupted images. This article aims to study some detection and filtering algorithms which remove RVIN in images. In addition to some state-of-the-art detection and filtering algorithms, a new detection technique, measures of dispersion (MOD) algorithm, for removing very high-density RVIN proposed by authors is also compared with existing methods. In the detection stage, rank order absolute difference, rank order logarithmic difference, adaptive switching median, triangle-based linear interpolation, and MOD algorithms are considered. Median filter, fuzzy switching median filter, and fuzzy switching weighted median filter are used for filtering followed by the detection algorithms. Comparative studies in terms of peak signal-to-noise ratio and structural similarity have been devised to evaluate the performance of various filtering schemes.

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.

Adaptive, Noise-Exclusive and Assessment-Based Fuzzy Switching Median Filter For High Intensity Impulse Noise

The article proposes an adaptive, noise-exclusive and assessment-based fuzzy switching median filter for high intensity impulse noise. The filter adaptively changes the size of sliding window in accordance with noise intensity and noise-exclusive operation is performed only on noise-free pixels. These steps thus preserve the image details effectively. Judicious assessment of the previously restored pixels is made in order to use them in replacing the noisy pixels and further improve the efficiency of filter. With these possibilities, fuzzy switching median filter-based approach is also incorporated for improving the performance. Experimental results justified the efficacy of the proposed fuzzy-based filtering method qualitatively and quantitatively for twelve different highly corrupted noisy images, and showed significant improvement to existing algorithms.

Novel Fuzzy Filters for Noise Suppression from Digital Grey and Color Images

Images are rich information carriers and (such as medical images) are normally contaminated by additive and substitutive noise which makes the extraction of features (and clinical data analysis) difficult. Hence to enhance the image quality prior to post processing, image pre-processing operations such as de-noising with linear and non-linear filters have been applied traditionally. Recently nonlinear filtering techniques have been assumed a lot of significance as they are capable of suppressing the effects of substitutive (salt and pepper impulsive noise of low to high noise levels) and additive (Gaussian noise of low to medium noise levels) noise types and to preserve the important signal/image details such as edges and fine details and suppress the degradations occurring at the time of image/signal formation or transmission through nonlinear channels, during storage and retrieval. Broadly speaking, image filters exist in transform and spatial domains. Spatial domain nonlinear filters are more versatile than their counterparts, namely linear filters. Spatial domain nonlinear fuzzy classical filters are simply modification/extension of the classical median and moving average filtering approaches, offer several advantages over classical nonlinear filters, and using simple fuzzy rules it is easy to realize them. They are also capable of reasoning with vague and uncertain information. Work presented in this paper deals with nonlinear median based and linear average based fuzzy filters and aims at fulfilling three objectives, viz; (i) To systematically study the performance of classical nonlinear median and fuzzy median and average filters for the removal of impulse and Gaussian noise from gray and color images that have been corrupted from low to high values of noise and to present an experimental review to identify the best algorithm within the frame work of classical fuzzy median filters. (ii)To propose : (a) an impulse classifier based fuzzy switching median filter and (b) the design of a multi pass cascaded fuzzy filter for noise cancellation, and explore their applications to reduce noise in images with random and impulse characteristics. Finally to conclude the work a comparative study is done and the computational aspects are analyzed with the help of mean square error (MSE), peak signal to noise ratio (PSNR), and 2D correlation (COR) and some future solutions are proposed.

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels. DOI: http://dx.doi.org/10.11591/ijece.v4i4.6253

Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction

A new model for impulse noise reduction with a recursive and noise-exclusive fuzzy switching median filter is proposed. The filter uses a S-type membership function for fuzzifying the noisy input variable and then estimates a correction label for it that aims at canceling the noisy effect. The fuzzification process provides better smoothing and generalization that improve the performance of the filter. The recursive and noise-exclusive operations further enhance the noise reduction capability of the filter. The recursive operation replaces the current pixel with the filtered pixel(s) and the noise-exclusive filtering uses only noise-free pixel(s) of the working window. The net effect of the filtering process thus preserves the sharp edges and fine details of the image in a more effective manner. The superiority of the proposed model to others in removing high density noise is established both quantitatively and qualitatively with various benchmarks and real-time test data sets. With the incorporation of the functionalities like fuzzy reasoning and noise-exclusive operations in the filtering process, the model becomes more efficient and fast. The applicability of the model thus opens ample scopes for hardware implementation in many electronics products that deal with images and videos.

A novel fuzzy system for edge detection in noisy image using bacterial foraging

Bio-inspired edge detection using fuzzy logic has achieved great attention in the recent years. The bacterial foraging (BF) algorithm, introduced in Passino (IEEE Control Syst Mag 22(3):52---67, 2002) is one of the powerful bio-inspired optimization algorithms. It attempts to imitate a single bacterium or groups of E. Coli bacteria. In BF algorithm, a set of bacteria forages towards a nutrient rich medium to get more nutrients. A new edge detection technique is proposed to deal with the noisy image using fuzzy derivative and bacterial foraging algorithm. The bacteria detect edge pixels as well as noisy pixels in its path during the foraging. The new fuzzy inference rules are devised and the direction of movement of each bacterium is found using these rules. During the foraging if a bacterium encounters a noisy pixel, it first removes the noisy pixel using an adaptive fuzzy switching median filter in Toh and Isa (IEEE Signal Process Lett 17(3):281---284, 2010). If the bacterium does not encounter any noisy pixel then it searches only the edge pixel in the image and draws the edge map. This approach can detect the edges in an image in the presence of impulse noise up to 30%.

Cluster-based adaptive fuzzy switching median filter for universal impulse noise reduction

In this paper, we present a novel method for the removal of impulse noise from digital images. The proposed filter, called the Cluster-based Adaptive Fuzzy Switching Median (CAFSM), is composed of a cascaded easyto- implement impulse detector and a detail preserving noise filter. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to many existing filters that only focus on a particular impulse noise model, the CAFSM filter is capable of filtering all kinds of impulse noise -the random-valued and/or fixed-valued impulse noise models. Extensive simulations conducted on 100 monochrome images under a wide range of noise densities show that the CAFSM filter substantially outperforms other state-of-the-art impulse noise filters. Furthermore, the relatively fast processing time suggests the CAFSM filter’s applicability in consumer electronic products such as digital cameras <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected “noise pixels” will then be subjected to the second stage of the filtering action, while “noise-free pixels” are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature. </para>

Salt-and-pepper noise detection and reduction using fuzzy switching median filter

This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.